fix: Support list[BaseModel] annotation about FunctionTool args

src/google/adk/tools/function_tool.py

@@ -102,6 +102,7 @@ def _preprocess_args(self, args: dict[str, Any]) -> dict[str, Any]:
 
     Currently handles:
     - Converting JSON dictionaries to Pydantic model instances where expected
+    - Converting lists of JSON dictionaries to lists of Pydantic model instances
 
     Future extensions could include:
     - Type coercion for other complex types
@@ -129,8 +130,39 @@ def _preprocess_args(self, args: dict[str, Any]) -> dict[str, Any]:
           if len(non_none_types) == 1:
             target_type = non_none_types[0]
 
+        # Check if the target type is a list
+        if get_origin(target_type) is list:
+          list_args = get_args(target_type)
+          if list_args:
+            element_type = list_args[0]
+
+            # Check if the element type is a Pydantic model
+            if inspect.isclass(element_type) and issubclass(
+                element_type, pydantic.BaseModel
+            ):
+              # Skip conversion if the value is None
+              if args[param_name] is None:
+                continue
+
+              # Convert list elements to Pydantic models
+              if isinstance(args[param_name], list):
+                converted_list = []
+                for item in args[param_name]:
+                  try:
+                    converted_list.append(element_type.model_validate(item))
+                  except Exception as e:
+                    logger.warning(
+                        f"Failed to convert item in '{param_name}' to Pydantic"
+                        f' model {element_type.__name__}: {e}'
+                    )
+
+                    # Keep the original value if conversion fails
+                    converted_list.append(item)
+
+                converted_args[param_name] = converted_list
+
         # Check if the target type is a Pydantic model
-        if inspect.isclass(target_type) and issubclass(
+        elif inspect.isclass(target_type) and issubclass(
             target_type, pydantic.BaseModel
         ):
           # Skip conversion if the value is None and the parameter is Optional
@@ -146,7 +178,7 @@ def _preprocess_args(self, args: dict[str, Any]) -> dict[str, Any]:
             except Exception as e:
               logger.warning(
                   f"Failed to convert argument '{param_name}' to Pydantic model"
-                  f' {target_type.__name__}: {e}'
+                  f' model {target_type.__name__}: {e}'
               )
               # Keep the original value if conversion fails
               pass

tests/unittests/tools/test_function_tool_pydantic.py

@@ -280,5 +280,131 @@ async def test_run_async_with_optional_pydantic_models():
   assert result["theme"] == "dark"
   assert result["notifications"] is True
   assert result["preferences_type"] == "PreferencesModel"
-  assert result["preferences_type"] == "PreferencesModel"
-  assert result["preferences_type"] == "PreferencesModel"
+
+
+def function_with_list_of_pydantic_models(users: list[UserModel]) -> dict:
+  """Function that takes a list of Pydantic models."""
+  return {
+      "count": len(users),
+      "names": [user.name for user in users],
+      "ages": [user.age for user in users],
+      "types": [type(user).__name__ for user in users],
+  }
+
+
+def function_with_optional_list_of_pydantic_models(
+    users: Optional[list[UserModel]] = None,
+) -> dict:
+  """Function that takes an optional list of Pydantic models."""
+  if users is None:
+    return {"count": 0, "names": []}
+  return {
+      "count": len(users),
+      "names": [user.name for user in users],
+  }
+
+
+def test_preprocess_args_with_list_of_dicts_to_pydantic_models():
+  """Test _preprocess_args converts list of dicts to list of Pydantic models."""
+  tool = FunctionTool(function_with_list_of_pydantic_models)
+
+  input_args = {
+      "users": [
+          {"name": "Alice", "age": 30, "email": "[email protected]"},
+          {"name": "Bob", "age": 25},
+          {"name": "Charlie", "age": 35, "email": "[email protected]"},
+      ]
+  }
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check that the list of dicts was converted to a list of Pydantic models
+  assert "users" in processed_args
+  users = processed_args["users"]
+  assert isinstance(users, list)
+  assert len(users) == 3
+
+  # Check each element is a Pydantic model with correct data
+  assert isinstance(users[0], UserModel)
+  assert users[0].name == "Alice"
+  assert users[0].age == 30
+  assert users[0].email == "[email protected]"
+
+  assert isinstance(users[1], UserModel)
+  assert users[1].name == "Bob"
+  assert users[1].age == 25
+  assert users[1].email is None
+
+  assert isinstance(users[2], UserModel)
+  assert users[2].name == "Charlie"
+  assert users[2].age == 35
+  assert users[2].email == "[email protected]"
+
+
+def test_preprocess_args_with_optional_list_of_pydantic_models_none():
+  """Test _preprocess_args handles None for optional list parameter."""
+  tool = FunctionTool(function_with_optional_list_of_pydantic_models)
+
+  input_args = {"users": None}
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check that None is preserved
+  assert "users" in processed_args
+  assert processed_args["users"] is None
+
+
+def test_preprocess_args_with_optional_list_of_pydantic_models_with_data():
+  """Test _preprocess_args converts list for optional list parameter."""
+  tool = FunctionTool(function_with_optional_list_of_pydantic_models)
+
+  input_args = {
+      "users": [
+          {"name": "Alice", "age": 30},
+          {"name": "Bob", "age": 25},
+      ]
+  }
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check conversion
+  assert "users" in processed_args
+  users = processed_args["users"]
+  assert len(users) == 2
+  assert all(isinstance(user, UserModel) for user in users)
+  assert users[0].name == "Alice"
+  assert users[1].name == "Bob"
+
+
+def test_preprocess_args_with_list_keeps_invalid_items_as_original():
+  """Test _preprocess_args keeps original data for items that fail validation."""
+  tool = FunctionTool(function_with_list_of_pydantic_models)
+
+  input_args = {
+      "users": [
+          {"name": "Alice", "age": 30},
+          {"name": "Invalid"},  # Missing required 'age' field
+          {"name": "Bob", "age": 25},
+      ]
+  }
+
+  processed_args = tool._preprocess_args(input_args)
+
+  # Check that all items are preserved
+  assert "users" in processed_args
+  users = processed_args["users"]
+  assert len(users) == 3  # All items preserved
+
+  # First item should be converted to UserModel
+  assert isinstance(users[0], UserModel)
+  assert users[0].name == "Alice"
+  assert users[0].age == 30
+
+  # Second item should remain as dict (failed validation)
+  assert isinstance(users[1], dict)
+  assert users[1] == {"name": "Invalid"}
+
+  # Third item should be converted to UserModel
+  assert isinstance(users[2], UserModel)
+  assert users[2].name == "Bob"
+  assert users[2].age == 25